基于強(qiáng)散射點(diǎn)在線估計(jì)的距離擴(kuò)展目標(biāo)檢測(cè)方法

郭鵬程; 劉崢; 羅丁利; 李儉樸

doi:10.11999/JEIT190417

基于強(qiáng)散射點(diǎn)在線估計(jì)的距離擴(kuò)展目標(biāo)檢測(cè)方法

doi: 10.11999/JEIT190417

郭鵬程^{1, 2},
劉崢^1, ,,
羅丁利²,
李儉樸¹

1.
西安電子科技大學(xué)雷達(dá)信號(hào)處理國(guó)家重點(diǎn)實(shí)驗(yàn)室西安 710071
2.
西安電子工程研究所西安 710100

詳細(xì)信息

作者簡(jiǎn)介:
郭鵬程：男，1983年生，高級(jí)工程師，博士生，研究方向?yàn)槔走_(dá)目標(biāo)檢測(cè)與識(shí)別

劉崢：男，1964年生，教授，研究方向?yàn)槔走_(dá)信號(hào)處理的理論與系統(tǒng)設(shè)計(jì)、雷達(dá)精確制導(dǎo)技術(shù)、多傳感器融合等

羅丁利：男，1974年生，研究員，研究方向?yàn)槔走_(dá)信號(hào)處理、目標(biāo)分類(lèi)識(shí)別技術(shù)

李儉樸：男，1994年生，碩士生，研究方向?yàn)槔走_(dá)目標(biāo)檢測(cè)

通訊作者:
劉崢　lz@xidian.edu.cn

中圖分類(lèi)號(hào): TN957.51
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2019-06-06
- 修回日期: 2019-09-07
- 網(wǎng)絡(luò)出版日期: 2019-09-19
- 刊出日期: 2020-06-04

Range Spread Target Detection Based on OnlineEstimation of Strong Scattering Points

Pengcheng GUO^{1, 2},
Zheng LIU^{1
, ,},
Dingli LUO²,
Jianpu LI¹

1.
National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
2.
Xi'an Electronic Engineering Research Institute, Xi’an 710100, China

摘要

摘要:
傳統(tǒng)的距離擴(kuò)展目標(biāo)檢測(cè)一般在散射點(diǎn)密度或散射點(diǎn)數(shù)量先驗(yàn)條件下完成，在目標(biāo)散射點(diǎn)信息完全未知時(shí)檢測(cè)性能會(huì)大幅降低。針對(duì)這個(gè)問(wèn)題，該文提出一種基于強(qiáng)散射點(diǎn)在線估計(jì)的距離擴(kuò)展目標(biāo)檢測(cè)方法(OESS-RSTD)，該方法利用機(jī)器學(xué)習(xí)中的無(wú)監(jiān)督聚類(lèi)算法在線估計(jì)強(qiáng)散射點(diǎn)數(shù)量以及首次檢測(cè)門(mén)限，然后再結(jié)合虛警率，確定2次檢測(cè)門(mén)限，最后通過(guò)兩次門(mén)限檢測(cè)完成目標(biāo)有無(wú)的判決。該文分別利用仿真數(shù)據(jù)和實(shí)測(cè)數(shù)據(jù)進(jìn)行了試驗(yàn)驗(yàn)證，并和其他算法進(jìn)行了試驗(yàn)對(duì)比，通過(guò)虛警概率一定時(shí)的信噪比(SNR)-檢測(cè)概率曲線驗(yàn)證了該文所提方法相對(duì)于傳統(tǒng)算法有更高的穩(wěn)健性，且該方法不需要目標(biāo)散射點(diǎn)的任何先驗(yàn)信息。
- 高分辨雷達(dá) /
- 擴(kuò)展目標(biāo)檢測(cè) /
- 聚類(lèi) /
- 強(qiáng)散射點(diǎn)估計(jì)
Abstract:
The traditional range-extended target detection is usually completed under the condition of scattering point density or scattering point number priori. The detection performance is greatly reduced when the scattering point information of the target is completely unknown. To solve this problem, a Range Spread Target Detection method based on Online Estimation of Strong Scattering(OESS-RSTD) points is proposed. Firstly, the unsupervised clustering algorithm in machine learning is used to estimate the number of strong scattering points and the first detection threshold adaptively. Then, the second detection threshold is determined according to false alarm rate. Finally, the existence of the target is determined through two detection thresholds. The simulation data and the measured data are used to verify and compare with other algorithms. By comparing the Signal-to-Noise Ratio (SNR) -detection probability curves of various methods with a given false alarm probability, it is verified that the proposed method has higher robustness than the traditional algorithm, and the method does not need any priori information of target scattering points.
- High resolution radar /
- Extended target detection /
- Clustering /
- Strong scattering point estimation

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圖 1 檢測(cè)器各區(qū)域的示意圖

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圖 2 非先驗(yàn)依賴(lài)的擴(kuò)展目標(biāo)檢測(cè)流程圖

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圖 3 卡車(chē)典型姿態(tài)的高分辨距離像

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圖 4 基于4種仿真模型的檢測(cè)性能對(duì)比

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圖 5 基于實(shí)測(cè)數(shù)據(jù)的檢測(cè)性能對(duì)比結(jié)果

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表 1 4種典型散射點(diǎn)模型

編號(hào)	散射點(diǎn)分布特點(diǎn)	名稱(chēng)
模型1	1個(gè)強(qiáng)散射點(diǎn)，占全部能量	單散射點(diǎn)
模型2	10個(gè)散射點(diǎn)，一個(gè)強(qiáng)散射點(diǎn)占50%能量，其他散射點(diǎn)占各占5.556%能量	稀疏多散射點(diǎn)
模型3	32個(gè)散射點(diǎn)，兩個(gè)強(qiáng)散射點(diǎn)各占25%，其他散射點(diǎn)占各占1.66%能量	密集非均勻多散射點(diǎn)
模型4	32個(gè)散射點(diǎn)，均勻分布，各占3.125%能量	密集均勻散射點(diǎn)

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